1518 lines
47 KiB
Python
1518 lines
47 KiB
Python
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"""
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**********
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Matplotlib
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**********
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Draw networks with matplotlib.
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Examples
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--------
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>>> G = nx.complete_graph(5)
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>>> nx.draw(G)
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See Also
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--------
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- :doc:`matplotlib <matplotlib:index>`
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- :func:`matplotlib.pyplot.scatter`
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- :obj:`matplotlib.patches.FancyArrowPatch`
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"""
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from numbers import Number
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import networkx as nx
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from networkx.drawing.layout import (
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circular_layout,
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kamada_kawai_layout,
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planar_layout,
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random_layout,
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shell_layout,
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spectral_layout,
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spring_layout,
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)
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__all__ = [
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"draw",
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"draw_networkx",
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"draw_networkx_nodes",
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"draw_networkx_edges",
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"draw_networkx_labels",
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"draw_networkx_edge_labels",
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"draw_circular",
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"draw_kamada_kawai",
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"draw_random",
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"draw_spectral",
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"draw_spring",
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"draw_planar",
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"draw_shell",
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]
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def draw(G, pos=None, ax=None, **kwds):
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"""Draw the graph G with Matplotlib.
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Draw the graph as a simple representation with no node
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labels or edge labels and using the full Matplotlib figure area
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and no axis labels by default. See draw_networkx() for more
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full-featured drawing that allows title, axis labels etc.
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Parameters
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----------
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G : graph
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A networkx graph
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pos : dictionary, optional
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A dictionary with nodes as keys and positions as values.
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If not specified a spring layout positioning will be computed.
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See :py:mod:`networkx.drawing.layout` for functions that
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compute node positions.
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ax : Matplotlib Axes object, optional
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Draw the graph in specified Matplotlib axes.
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kwds : optional keywords
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See networkx.draw_networkx() for a description of optional keywords.
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Examples
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--------
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>>> G = nx.dodecahedral_graph()
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>>> nx.draw(G)
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>>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout
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See Also
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--------
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draw_networkx
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draw_networkx_nodes
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draw_networkx_edges
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draw_networkx_labels
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draw_networkx_edge_labels
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Notes
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-----
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This function has the same name as pylab.draw and pyplot.draw
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so beware when using `from networkx import *`
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since you might overwrite the pylab.draw function.
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With pyplot use
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>>> import matplotlib.pyplot as plt
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>>> G = nx.dodecahedral_graph()
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>>> nx.draw(G) # networkx draw()
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>>> plt.draw() # pyplot draw()
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Also see the NetworkX drawing examples at
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https://networkx.org/documentation/latest/auto_examples/index.html
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"""
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import matplotlib.pyplot as plt
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if ax is None:
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cf = plt.gcf()
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else:
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cf = ax.get_figure()
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cf.set_facecolor("w")
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if ax is None:
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if cf.axes:
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ax = cf.gca()
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else:
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ax = cf.add_axes((0, 0, 1, 1))
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if "with_labels" not in kwds:
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kwds["with_labels"] = "labels" in kwds
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draw_networkx(G, pos=pos, ax=ax, **kwds)
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ax.set_axis_off()
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plt.draw_if_interactive()
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return
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def draw_networkx(G, pos=None, arrows=None, with_labels=True, **kwds):
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r"""Draw the graph G using Matplotlib.
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Draw the graph with Matplotlib with options for node positions,
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labeling, titles, and many other drawing features.
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See draw() for simple drawing without labels or axes.
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Parameters
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----------
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G : graph
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A networkx graph
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pos : dictionary, optional
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A dictionary with nodes as keys and positions as values.
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If not specified a spring layout positioning will be computed.
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See :py:mod:`networkx.drawing.layout` for functions that
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compute node positions.
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arrows : bool or None, optional (default=None)
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If `None`, directed graphs draw arrowheads with
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`~matplotlib.patches.FancyArrowPatch`, while undirected graphs draw edges
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via `~matplotlib.collections.LineCollection` for speed.
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If `True`, draw arrowheads with FancyArrowPatches (bendable and stylish).
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If `False`, draw edges using LineCollection (linear and fast).
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For directed graphs, if True draw arrowheads.
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Note: Arrows will be the same color as edges.
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arrowstyle : str (default='-\|>' for directed graphs)
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For directed graphs, choose the style of the arrowsheads.
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For undirected graphs default to '-'
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See `matplotlib.patches.ArrowStyle` for more options.
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arrowsize : int or list (default=10)
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For directed graphs, choose the size of the arrow head's length and
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width. A list of values can be passed in to assign a different size for arrow head's length and width.
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See `matplotlib.patches.FancyArrowPatch` for attribute `mutation_scale`
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for more info.
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with_labels : bool (default=True)
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Set to True to draw labels on the nodes.
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ax : Matplotlib Axes object, optional
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Draw the graph in the specified Matplotlib axes.
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nodelist : list (default=list(G))
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Draw only specified nodes
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edgelist : list (default=list(G.edges()))
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Draw only specified edges
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node_size : scalar or array (default=300)
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Size of nodes. If an array is specified it must be the
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same length as nodelist.
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node_color : color or array of colors (default='#1f78b4')
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Node color. Can be a single color or a sequence of colors with the same
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length as nodelist. Color can be string or rgb (or rgba) tuple of
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floats from 0-1. If numeric values are specified they will be
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mapped to colors using the cmap and vmin,vmax parameters. See
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matplotlib.scatter for more details.
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node_shape : string (default='o')
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The shape of the node. Specification is as matplotlib.scatter
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marker, one of 'so^>v<dph8'.
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alpha : float or None (default=None)
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The node and edge transparency
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cmap : Matplotlib colormap, optional
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Colormap for mapping intensities of nodes
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vmin,vmax : float, optional
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Minimum and maximum for node colormap scaling
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linewidths : scalar or sequence (default=1.0)
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Line width of symbol border
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width : float or array of floats (default=1.0)
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Line width of edges
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edge_color : color or array of colors (default='k')
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Edge color. Can be a single color or a sequence of colors with the same
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length as edgelist. Color can be string or rgb (or rgba) tuple of
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floats from 0-1. If numeric values are specified they will be
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mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
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edge_cmap : Matplotlib colormap, optional
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Colormap for mapping intensities of edges
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edge_vmin,edge_vmax : floats, optional
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Minimum and maximum for edge colormap scaling
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style : string (default=solid line)
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Edge line style e.g.: '-', '--', '-.', ':'
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or words like 'solid' or 'dashed'.
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(See `matplotlib.patches.FancyArrowPatch`: `linestyle`)
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labels : dictionary (default=None)
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Node labels in a dictionary of text labels keyed by node
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font_size : int (default=12 for nodes, 10 for edges)
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Font size for text labels
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font_color : string (default='k' black)
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Font color string
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font_weight : string (default='normal')
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Font weight
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font_family : string (default='sans-serif')
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Font family
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label : string, optional
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Label for graph legend
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kwds : optional keywords
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See networkx.draw_networkx_nodes(), networkx.draw_networkx_edges(), and
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networkx.draw_networkx_labels() for a description of optional keywords.
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Notes
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-----
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For directed graphs, arrows are drawn at the head end. Arrows can be
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turned off with keyword arrows=False.
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Examples
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--------
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>>> G = nx.dodecahedral_graph()
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>>> nx.draw(G)
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>>> nx.draw(G, pos=nx.spring_layout(G)) # use spring layout
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>>> import matplotlib.pyplot as plt
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>>> limits = plt.axis("off") # turn off axis
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Also see the NetworkX drawing examples at
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https://networkx.org/documentation/latest/auto_examples/index.html
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See Also
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--------
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draw
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draw_networkx_nodes
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draw_networkx_edges
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draw_networkx_labels
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draw_networkx_edge_labels
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"""
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from inspect import signature
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import matplotlib.pyplot as plt
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# Get all valid keywords by inspecting the signatures of draw_networkx_nodes,
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# draw_networkx_edges, draw_networkx_labels
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valid_node_kwds = signature(draw_networkx_nodes).parameters.keys()
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valid_edge_kwds = signature(draw_networkx_edges).parameters.keys()
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valid_label_kwds = signature(draw_networkx_labels).parameters.keys()
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# Create a set with all valid keywords across the three functions and
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# remove the arguments of this function (draw_networkx)
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valid_kwds = (valid_node_kwds | valid_edge_kwds | valid_label_kwds) - {
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"G",
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"pos",
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"arrows",
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"with_labels",
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}
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if any([k not in valid_kwds for k in kwds]):
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invalid_args = ", ".join([k for k in kwds if k not in valid_kwds])
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raise ValueError(f"Received invalid argument(s): {invalid_args}")
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node_kwds = {k: v for k, v in kwds.items() if k in valid_node_kwds}
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edge_kwds = {k: v for k, v in kwds.items() if k in valid_edge_kwds}
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label_kwds = {k: v for k, v in kwds.items() if k in valid_label_kwds}
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if pos is None:
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pos = nx.drawing.spring_layout(G) # default to spring layout
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draw_networkx_nodes(G, pos, **node_kwds)
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draw_networkx_edges(G, pos, arrows=arrows, **edge_kwds)
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if with_labels:
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draw_networkx_labels(G, pos, **label_kwds)
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plt.draw_if_interactive()
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def draw_networkx_nodes(
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G,
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pos,
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nodelist=None,
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node_size=300,
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node_color="#1f78b4",
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node_shape="o",
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alpha=None,
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cmap=None,
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vmin=None,
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vmax=None,
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ax=None,
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linewidths=None,
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edgecolors=None,
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label=None,
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margins=None,
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):
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"""Draw the nodes of the graph G.
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This draws only the nodes of the graph G.
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Parameters
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----------
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G : graph
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A networkx graph
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pos : dictionary
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A dictionary with nodes as keys and positions as values.
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Positions should be sequences of length 2.
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ax : Matplotlib Axes object, optional
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Draw the graph in the specified Matplotlib axes.
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nodelist : list (default list(G))
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Draw only specified nodes
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node_size : scalar or array (default=300)
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Size of nodes. If an array it must be the same length as nodelist.
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node_color : color or array of colors (default='#1f78b4')
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Node color. Can be a single color or a sequence of colors with the same
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length as nodelist. Color can be string or rgb (or rgba) tuple of
|
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floats from 0-1. If numeric values are specified they will be
|
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mapped to colors using the cmap and vmin,vmax parameters. See
|
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matplotlib.scatter for more details.
|
||
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node_shape : string (default='o')
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The shape of the node. Specification is as matplotlib.scatter
|
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marker, one of 'so^>v<dph8'.
|
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alpha : float or array of floats (default=None)
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The node transparency. This can be a single alpha value,
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in which case it will be applied to all the nodes of color. Otherwise,
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if it is an array, the elements of alpha will be applied to the colors
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in order (cycling through alpha multiple times if necessary).
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cmap : Matplotlib colormap (default=None)
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Colormap for mapping intensities of nodes
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vmin,vmax : floats or None (default=None)
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Minimum and maximum for node colormap scaling
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linewidths : [None | scalar | sequence] (default=1.0)
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Line width of symbol border
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edgecolors : [None | scalar | sequence] (default = node_color)
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Colors of node borders
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label : [None | string]
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Label for legend
|
||
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margins : float or 2-tuple, optional
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||
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Sets the padding for axis autoscaling. Increase margin to prevent
|
||
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clipping for nodes that are near the edges of an image. Values should
|
||
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be in the range ``[0, 1]``. See :meth:`matplotlib.axes.Axes.margins`
|
||
|
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for details. The default is `None`, which uses the Matplotlib default.
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||
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Returns
|
||
|
|
-------
|
||
|
|
matplotlib.collections.PathCollection
|
||
|
|
`PathCollection` of the nodes.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> G = nx.dodecahedral_graph()
|
||
|
|
>>> nodes = nx.draw_networkx_nodes(G, pos=nx.spring_layout(G))
|
||
|
|
|
||
|
|
Also see the NetworkX drawing examples at
|
||
|
|
https://networkx.org/documentation/latest/auto_examples/index.html
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
draw
|
||
|
|
draw_networkx
|
||
|
|
draw_networkx_edges
|
||
|
|
draw_networkx_labels
|
||
|
|
draw_networkx_edge_labels
|
||
|
|
"""
|
||
|
|
from collections.abc import Iterable
|
||
|
|
|
||
|
|
import matplotlib as mpl
|
||
|
|
import matplotlib.collections # call as mpl.collections
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
if ax is None:
|
||
|
|
ax = plt.gca()
|
||
|
|
|
||
|
|
if nodelist is None:
|
||
|
|
nodelist = list(G)
|
||
|
|
|
||
|
|
if len(nodelist) == 0: # empty nodelist, no drawing
|
||
|
|
return mpl.collections.PathCollection(None)
|
||
|
|
|
||
|
|
try:
|
||
|
|
xy = np.asarray([pos[v] for v in nodelist])
|
||
|
|
except KeyError as err:
|
||
|
|
raise nx.NetworkXError(f"Node {err} has no position.") from err
|
||
|
|
|
||
|
|
if isinstance(alpha, Iterable):
|
||
|
|
node_color = apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)
|
||
|
|
alpha = None
|
||
|
|
|
||
|
|
node_collection = ax.scatter(
|
||
|
|
xy[:, 0],
|
||
|
|
xy[:, 1],
|
||
|
|
s=node_size,
|
||
|
|
c=node_color,
|
||
|
|
marker=node_shape,
|
||
|
|
cmap=cmap,
|
||
|
|
vmin=vmin,
|
||
|
|
vmax=vmax,
|
||
|
|
alpha=alpha,
|
||
|
|
linewidths=linewidths,
|
||
|
|
edgecolors=edgecolors,
|
||
|
|
label=label,
|
||
|
|
)
|
||
|
|
ax.tick_params(
|
||
|
|
axis="both",
|
||
|
|
which="both",
|
||
|
|
bottom=False,
|
||
|
|
left=False,
|
||
|
|
labelbottom=False,
|
||
|
|
labelleft=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
if margins is not None:
|
||
|
|
if isinstance(margins, Iterable):
|
||
|
|
ax.margins(*margins)
|
||
|
|
else:
|
||
|
|
ax.margins(margins)
|
||
|
|
|
||
|
|
node_collection.set_zorder(2)
|
||
|
|
return node_collection
|
||
|
|
|
||
|
|
|
||
|
|
def draw_networkx_edges(
|
||
|
|
G,
|
||
|
|
pos,
|
||
|
|
edgelist=None,
|
||
|
|
width=1.0,
|
||
|
|
edge_color="k",
|
||
|
|
style="solid",
|
||
|
|
alpha=None,
|
||
|
|
arrowstyle=None,
|
||
|
|
arrowsize=10,
|
||
|
|
edge_cmap=None,
|
||
|
|
edge_vmin=None,
|
||
|
|
edge_vmax=None,
|
||
|
|
ax=None,
|
||
|
|
arrows=None,
|
||
|
|
label=None,
|
||
|
|
node_size=300,
|
||
|
|
nodelist=None,
|
||
|
|
node_shape="o",
|
||
|
|
connectionstyle="arc3",
|
||
|
|
min_source_margin=0,
|
||
|
|
min_target_margin=0,
|
||
|
|
):
|
||
|
|
r"""Draw the edges of the graph G.
|
||
|
|
|
||
|
|
This draws only the edges of the graph G.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : graph
|
||
|
|
A networkx graph
|
||
|
|
|
||
|
|
pos : dictionary
|
||
|
|
A dictionary with nodes as keys and positions as values.
|
||
|
|
Positions should be sequences of length 2.
|
||
|
|
|
||
|
|
edgelist : collection of edge tuples (default=G.edges())
|
||
|
|
Draw only specified edges
|
||
|
|
|
||
|
|
width : float or array of floats (default=1.0)
|
||
|
|
Line width of edges
|
||
|
|
|
||
|
|
edge_color : color or array of colors (default='k')
|
||
|
|
Edge color. Can be a single color or a sequence of colors with the same
|
||
|
|
length as edgelist. Color can be string or rgb (or rgba) tuple of
|
||
|
|
floats from 0-1. If numeric values are specified they will be
|
||
|
|
mapped to colors using the edge_cmap and edge_vmin,edge_vmax parameters.
|
||
|
|
|
||
|
|
style : string or array of strings (default='solid')
|
||
|
|
Edge line style e.g.: '-', '--', '-.', ':'
|
||
|
|
or words like 'solid' or 'dashed'.
|
||
|
|
Can be a single style or a sequence of styles with the same
|
||
|
|
length as the edge list.
|
||
|
|
If less styles than edges are given the styles will cycle.
|
||
|
|
If more styles than edges are given the styles will be used sequentially
|
||
|
|
and not be exhausted.
|
||
|
|
Also, `(offset, onoffseq)` tuples can be used as style instead of a strings.
|
||
|
|
(See `matplotlib.patches.FancyArrowPatch`: `linestyle`)
|
||
|
|
|
||
|
|
alpha : float or None (default=None)
|
||
|
|
The edge transparency
|
||
|
|
|
||
|
|
edge_cmap : Matplotlib colormap, optional
|
||
|
|
Colormap for mapping intensities of edges
|
||
|
|
|
||
|
|
edge_vmin,edge_vmax : floats, optional
|
||
|
|
Minimum and maximum for edge colormap scaling
|
||
|
|
|
||
|
|
ax : Matplotlib Axes object, optional
|
||
|
|
Draw the graph in the specified Matplotlib axes.
|
||
|
|
|
||
|
|
arrows : bool or None, optional (default=None)
|
||
|
|
If `None`, directed graphs draw arrowheads with
|
||
|
|
`~matplotlib.patches.FancyArrowPatch`, while undirected graphs draw edges
|
||
|
|
via `~matplotlib.collections.LineCollection` for speed.
|
||
|
|
If `True`, draw arrowheads with FancyArrowPatches (bendable and stylish).
|
||
|
|
If `False`, draw edges using LineCollection (linear and fast).
|
||
|
|
|
||
|
|
Note: Arrowheads will be the same color as edges.
|
||
|
|
|
||
|
|
arrowstyle : str (default='-\|>' for directed graphs)
|
||
|
|
For directed graphs and `arrows==True` defaults to '-\|>',
|
||
|
|
For undirected graphs default to '-'.
|
||
|
|
|
||
|
|
See `matplotlib.patches.ArrowStyle` for more options.
|
||
|
|
|
||
|
|
arrowsize : int (default=10)
|
||
|
|
For directed graphs, choose the size of the arrow head's length and
|
||
|
|
width. See `matplotlib.patches.FancyArrowPatch` for attribute
|
||
|
|
`mutation_scale` for more info.
|
||
|
|
|
||
|
|
connectionstyle : string (default="arc3")
|
||
|
|
Pass the connectionstyle parameter to create curved arc of rounding
|
||
|
|
radius rad. For example, connectionstyle='arc3,rad=0.2'.
|
||
|
|
See `matplotlib.patches.ConnectionStyle` and
|
||
|
|
`matplotlib.patches.FancyArrowPatch` for more info.
|
||
|
|
|
||
|
|
node_size : scalar or array (default=300)
|
||
|
|
Size of nodes. Though the nodes are not drawn with this function, the
|
||
|
|
node size is used in determining edge positioning.
|
||
|
|
|
||
|
|
nodelist : list, optional (default=G.nodes())
|
||
|
|
This provides the node order for the `node_size` array (if it is an array).
|
||
|
|
|
||
|
|
node_shape : string (default='o')
|
||
|
|
The marker used for nodes, used in determining edge positioning.
|
||
|
|
Specification is as a `matplotlib.markers` marker, e.g. one of 'so^>v<dph8'.
|
||
|
|
|
||
|
|
label : None or string
|
||
|
|
Label for legend
|
||
|
|
|
||
|
|
min_source_margin : int (default=0)
|
||
|
|
The minimum margin (gap) at the begining of the edge at the source.
|
||
|
|
|
||
|
|
min_target_margin : int (default=0)
|
||
|
|
The minimum margin (gap) at the end of the edge at the target.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
matplotlib.colections.LineCollection or a list of matplotlib.patches.FancyArrowPatch
|
||
|
|
If ``arrows=True``, a list of FancyArrowPatches is returned.
|
||
|
|
If ``arrows=False``, a LineCollection is returned.
|
||
|
|
If ``arrows=None`` (the default), then a LineCollection is returned if
|
||
|
|
`G` is undirected, otherwise returns a list of FancyArrowPatches.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
For directed graphs, arrows are drawn at the head end. Arrows can be
|
||
|
|
turned off with keyword arrows=False or by passing an arrowstyle without
|
||
|
|
an arrow on the end.
|
||
|
|
|
||
|
|
Be sure to include `node_size` as a keyword argument; arrows are
|
||
|
|
drawn considering the size of nodes.
|
||
|
|
|
||
|
|
Self-loops are always drawn with `~matplotlib.patches.FancyArrowPatch`
|
||
|
|
regardless of the value of `arrows` or whether `G` is directed.
|
||
|
|
When ``arrows=False`` or ``arrows=None`` and `G` is undirected, the
|
||
|
|
FancyArrowPatches corresponding to the self-loops are not explicitly
|
||
|
|
returned. They should instead be accessed via the ``Axes.patches``
|
||
|
|
attribute (see examples).
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> G = nx.dodecahedral_graph()
|
||
|
|
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
|
||
|
|
|
||
|
|
>>> G = nx.DiGraph()
|
||
|
|
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
|
||
|
|
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
|
||
|
|
>>> alphas = [0.3, 0.4, 0.5]
|
||
|
|
>>> for i, arc in enumerate(arcs): # change alpha values of arcs
|
||
|
|
... arc.set_alpha(alphas[i])
|
||
|
|
|
||
|
|
The FancyArrowPatches corresponding to self-loops are not always
|
||
|
|
returned, but can always be accessed via the ``patches`` attribute of the
|
||
|
|
`matplotlib.Axes` object.
|
||
|
|
|
||
|
|
>>> import matplotlib.pyplot as plt
|
||
|
|
>>> fig, ax = plt.subplots()
|
||
|
|
>>> G = nx.Graph([(0, 1), (0, 0)]) # Self-loop at node 0
|
||
|
|
>>> edge_collection = nx.draw_networkx_edges(G, pos=nx.circular_layout(G), ax=ax)
|
||
|
|
>>> self_loop_fap = ax.patches[0]
|
||
|
|
|
||
|
|
Also see the NetworkX drawing examples at
|
||
|
|
https://networkx.org/documentation/latest/auto_examples/index.html
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
draw
|
||
|
|
draw_networkx
|
||
|
|
draw_networkx_nodes
|
||
|
|
draw_networkx_labels
|
||
|
|
draw_networkx_edge_labels
|
||
|
|
|
||
|
|
"""
|
||
|
|
import matplotlib as mpl
|
||
|
|
import matplotlib.collections # call as mpl.collections
|
||
|
|
import matplotlib.colors # call as mpl.colors
|
||
|
|
import matplotlib.patches # call as mpl.patches
|
||
|
|
import matplotlib.path # call as mpl.path
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
# The default behavior is to use LineCollection to draw edges for
|
||
|
|
# undirected graphs (for performance reasons) and use FancyArrowPatches
|
||
|
|
# for directed graphs.
|
||
|
|
# The `arrows` keyword can be used to override the default behavior
|
||
|
|
|
||
|
|
if arrowstyle == None:
|
||
|
|
if G.is_directed():
|
||
|
|
arrowstyle = "-|>"
|
||
|
|
else:
|
||
|
|
arrowstyle = "-"
|
||
|
|
|
||
|
|
use_linecollection = not G.is_directed()
|
||
|
|
if arrows in (True, False):
|
||
|
|
use_linecollection = not arrows
|
||
|
|
|
||
|
|
if ax is None:
|
||
|
|
ax = plt.gca()
|
||
|
|
|
||
|
|
if edgelist is None:
|
||
|
|
edgelist = list(G.edges())
|
||
|
|
|
||
|
|
if len(edgelist) == 0: # no edges!
|
||
|
|
return []
|
||
|
|
|
||
|
|
if nodelist is None:
|
||
|
|
nodelist = list(G.nodes())
|
||
|
|
|
||
|
|
# FancyArrowPatch handles color=None different from LineCollection
|
||
|
|
if edge_color is None:
|
||
|
|
edge_color = "k"
|
||
|
|
edgelist_tuple = list(map(tuple, edgelist))
|
||
|
|
|
||
|
|
# set edge positions
|
||
|
|
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
|
||
|
|
|
||
|
|
# Check if edge_color is an array of floats and map to edge_cmap.
|
||
|
|
# This is the only case handled differently from matplotlib
|
||
|
|
if (
|
||
|
|
np.iterable(edge_color)
|
||
|
|
and (len(edge_color) == len(edge_pos))
|
||
|
|
and np.alltrue([isinstance(c, Number) for c in edge_color])
|
||
|
|
):
|
||
|
|
if edge_cmap is not None:
|
||
|
|
assert isinstance(edge_cmap, mpl.colors.Colormap)
|
||
|
|
else:
|
||
|
|
edge_cmap = plt.get_cmap()
|
||
|
|
if edge_vmin is None:
|
||
|
|
edge_vmin = min(edge_color)
|
||
|
|
if edge_vmax is None:
|
||
|
|
edge_vmax = max(edge_color)
|
||
|
|
color_normal = mpl.colors.Normalize(vmin=edge_vmin, vmax=edge_vmax)
|
||
|
|
edge_color = [edge_cmap(color_normal(e)) for e in edge_color]
|
||
|
|
|
||
|
|
def _draw_networkx_edges_line_collection():
|
||
|
|
edge_collection = mpl.collections.LineCollection(
|
||
|
|
edge_pos,
|
||
|
|
colors=edge_color,
|
||
|
|
linewidths=width,
|
||
|
|
antialiaseds=(1,),
|
||
|
|
linestyle=style,
|
||
|
|
alpha=alpha,
|
||
|
|
)
|
||
|
|
edge_collection.set_cmap(edge_cmap)
|
||
|
|
edge_collection.set_clim(edge_vmin, edge_vmax)
|
||
|
|
edge_collection.set_zorder(1) # edges go behind nodes
|
||
|
|
edge_collection.set_label(label)
|
||
|
|
ax.add_collection(edge_collection)
|
||
|
|
|
||
|
|
return edge_collection
|
||
|
|
|
||
|
|
def _draw_networkx_edges_fancy_arrow_patch():
|
||
|
|
# Note: Waiting for someone to implement arrow to intersection with
|
||
|
|
# marker. Meanwhile, this works well for polygons with more than 4
|
||
|
|
# sides and circle.
|
||
|
|
|
||
|
|
def to_marker_edge(marker_size, marker):
|
||
|
|
if marker in "s^>v<d": # `large` markers need extra space
|
||
|
|
return np.sqrt(2 * marker_size) / 2
|
||
|
|
else:
|
||
|
|
return np.sqrt(marker_size) / 2
|
||
|
|
|
||
|
|
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
|
||
|
|
arrow_collection = []
|
||
|
|
|
||
|
|
if isinstance(arrowsize, list):
|
||
|
|
if len(arrowsize) != len(edge_pos):
|
||
|
|
raise ValueError("arrowsize should have the same length as edgelist")
|
||
|
|
else:
|
||
|
|
mutation_scale = arrowsize # scale factor of arrow head
|
||
|
|
|
||
|
|
base_connection_style = mpl.patches.ConnectionStyle(connectionstyle)
|
||
|
|
|
||
|
|
# Fallback for self-loop scale. Left outside of _connectionstyle so it is
|
||
|
|
# only computed once
|
||
|
|
max_nodesize = np.array(node_size).max()
|
||
|
|
|
||
|
|
def _connectionstyle(posA, posB, *args, **kwargs):
|
||
|
|
# check if we need to do a self-loop
|
||
|
|
if np.all(posA == posB):
|
||
|
|
# Self-loops are scaled by view extent, except in cases the extent
|
||
|
|
# is 0, e.g. for a single node. In this case, fall back to scaling
|
||
|
|
# by the maximum node size
|
||
|
|
selfloop_ht = 0.005 * max_nodesize if h == 0 else h
|
||
|
|
# this is called with _screen space_ values so covert back
|
||
|
|
# to data space
|
||
|
|
data_loc = ax.transData.inverted().transform(posA)
|
||
|
|
v_shift = 0.1 * selfloop_ht
|
||
|
|
h_shift = v_shift * 0.5
|
||
|
|
# put the top of the loop first so arrow is not hidden by node
|
||
|
|
path = [
|
||
|
|
# 1
|
||
|
|
data_loc + np.asarray([0, v_shift]),
|
||
|
|
# 4 4 4
|
||
|
|
data_loc + np.asarray([h_shift, v_shift]),
|
||
|
|
data_loc + np.asarray([h_shift, 0]),
|
||
|
|
data_loc,
|
||
|
|
# 4 4 4
|
||
|
|
data_loc + np.asarray([-h_shift, 0]),
|
||
|
|
data_loc + np.asarray([-h_shift, v_shift]),
|
||
|
|
data_loc + np.asarray([0, v_shift]),
|
||
|
|
]
|
||
|
|
|
||
|
|
ret = mpl.path.Path(ax.transData.transform(path), [1, 4, 4, 4, 4, 4, 4])
|
||
|
|
# if not, fall back to the user specified behavior
|
||
|
|
else:
|
||
|
|
ret = base_connection_style(posA, posB, *args, **kwargs)
|
||
|
|
|
||
|
|
return ret
|
||
|
|
|
||
|
|
# FancyArrowPatch doesn't handle color strings
|
||
|
|
arrow_colors = mpl.colors.colorConverter.to_rgba_array(edge_color, alpha)
|
||
|
|
for i, (src, dst) in zip(fancy_edges_indices, edge_pos):
|
||
|
|
x1, y1 = src
|
||
|
|
x2, y2 = dst
|
||
|
|
shrink_source = 0 # space from source to tail
|
||
|
|
shrink_target = 0 # space from head to target
|
||
|
|
|
||
|
|
if isinstance(arrowsize, list):
|
||
|
|
# Scale each factor of each arrow based on arrowsize list
|
||
|
|
mutation_scale = arrowsize[i]
|
||
|
|
|
||
|
|
if np.iterable(node_size): # many node sizes
|
||
|
|
source, target = edgelist[i][:2]
|
||
|
|
source_node_size = node_size[nodelist.index(source)]
|
||
|
|
target_node_size = node_size[nodelist.index(target)]
|
||
|
|
shrink_source = to_marker_edge(source_node_size, node_shape)
|
||
|
|
shrink_target = to_marker_edge(target_node_size, node_shape)
|
||
|
|
else:
|
||
|
|
shrink_source = shrink_target = to_marker_edge(node_size, node_shape)
|
||
|
|
|
||
|
|
if shrink_source < min_source_margin:
|
||
|
|
shrink_source = min_source_margin
|
||
|
|
|
||
|
|
if shrink_target < min_target_margin:
|
||
|
|
shrink_target = min_target_margin
|
||
|
|
|
||
|
|
if len(arrow_colors) > i:
|
||
|
|
arrow_color = arrow_colors[i]
|
||
|
|
elif len(arrow_colors) == 1:
|
||
|
|
arrow_color = arrow_colors[0]
|
||
|
|
else: # Cycle through colors
|
||
|
|
arrow_color = arrow_colors[i % len(arrow_colors)]
|
||
|
|
|
||
|
|
if np.iterable(width):
|
||
|
|
if len(width) > i:
|
||
|
|
line_width = width[i]
|
||
|
|
else:
|
||
|
|
line_width = width[i % len(width)]
|
||
|
|
else:
|
||
|
|
line_width = width
|
||
|
|
|
||
|
|
if (
|
||
|
|
np.iterable(style)
|
||
|
|
and not isinstance(style, str)
|
||
|
|
and not isinstance(style, tuple)
|
||
|
|
):
|
||
|
|
if len(style) > i:
|
||
|
|
linestyle = style[i]
|
||
|
|
else: # Cycle through styles
|
||
|
|
linestyle = style[i % len(style)]
|
||
|
|
else:
|
||
|
|
linestyle = style
|
||
|
|
|
||
|
|
arrow = mpl.patches.FancyArrowPatch(
|
||
|
|
(x1, y1),
|
||
|
|
(x2, y2),
|
||
|
|
arrowstyle=arrowstyle,
|
||
|
|
shrinkA=shrink_source,
|
||
|
|
shrinkB=shrink_target,
|
||
|
|
mutation_scale=mutation_scale,
|
||
|
|
color=arrow_color,
|
||
|
|
linewidth=line_width,
|
||
|
|
connectionstyle=_connectionstyle,
|
||
|
|
linestyle=linestyle,
|
||
|
|
zorder=1,
|
||
|
|
) # arrows go behind nodes
|
||
|
|
|
||
|
|
arrow_collection.append(arrow)
|
||
|
|
ax.add_patch(arrow)
|
||
|
|
|
||
|
|
return arrow_collection
|
||
|
|
|
||
|
|
# compute initial view
|
||
|
|
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
|
||
|
|
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
|
||
|
|
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
|
||
|
|
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
|
||
|
|
w = maxx - minx
|
||
|
|
h = maxy - miny
|
||
|
|
|
||
|
|
# Draw the edges
|
||
|
|
if use_linecollection:
|
||
|
|
edge_viz_obj = _draw_networkx_edges_line_collection()
|
||
|
|
# Make sure selfloop edges are also drawn
|
||
|
|
selfloops_to_draw = [loop for loop in nx.selfloop_edges(G) if loop in edgelist]
|
||
|
|
if selfloops_to_draw:
|
||
|
|
fancy_edges_indices = [
|
||
|
|
edgelist_tuple.index(loop) for loop in selfloops_to_draw
|
||
|
|
]
|
||
|
|
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in selfloops_to_draw])
|
||
|
|
arrowstyle = "-"
|
||
|
|
_draw_networkx_edges_fancy_arrow_patch()
|
||
|
|
else:
|
||
|
|
fancy_edges_indices = range(len(edgelist))
|
||
|
|
edge_viz_obj = _draw_networkx_edges_fancy_arrow_patch()
|
||
|
|
|
||
|
|
# update view after drawing
|
||
|
|
padx, pady = 0.05 * w, 0.05 * h
|
||
|
|
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
|
||
|
|
ax.update_datalim(corners)
|
||
|
|
ax.autoscale_view()
|
||
|
|
|
||
|
|
ax.tick_params(
|
||
|
|
axis="both",
|
||
|
|
which="both",
|
||
|
|
bottom=False,
|
||
|
|
left=False,
|
||
|
|
labelbottom=False,
|
||
|
|
labelleft=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
return edge_viz_obj
|
||
|
|
|
||
|
|
|
||
|
|
def draw_networkx_labels(
|
||
|
|
G,
|
||
|
|
pos,
|
||
|
|
labels=None,
|
||
|
|
font_size=12,
|
||
|
|
font_color="k",
|
||
|
|
font_family="sans-serif",
|
||
|
|
font_weight="normal",
|
||
|
|
alpha=None,
|
||
|
|
bbox=None,
|
||
|
|
horizontalalignment="center",
|
||
|
|
verticalalignment="center",
|
||
|
|
ax=None,
|
||
|
|
clip_on=True,
|
||
|
|
):
|
||
|
|
"""Draw node labels on the graph G.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : graph
|
||
|
|
A networkx graph
|
||
|
|
|
||
|
|
pos : dictionary
|
||
|
|
A dictionary with nodes as keys and positions as values.
|
||
|
|
Positions should be sequences of length 2.
|
||
|
|
|
||
|
|
labels : dictionary (default={n: n for n in G})
|
||
|
|
Node labels in a dictionary of text labels keyed by node.
|
||
|
|
Node-keys in labels should appear as keys in `pos`.
|
||
|
|
If needed use: `{n:lab for n,lab in labels.items() if n in pos}`
|
||
|
|
|
||
|
|
font_size : int (default=12)
|
||
|
|
Font size for text labels
|
||
|
|
|
||
|
|
font_color : string (default='k' black)
|
||
|
|
Font color string
|
||
|
|
|
||
|
|
font_weight : string (default='normal')
|
||
|
|
Font weight
|
||
|
|
|
||
|
|
font_family : string (default='sans-serif')
|
||
|
|
Font family
|
||
|
|
|
||
|
|
alpha : float or None (default=None)
|
||
|
|
The text transparency
|
||
|
|
|
||
|
|
bbox : Matplotlib bbox, (default is Matplotlib's ax.text default)
|
||
|
|
Specify text box properties (e.g. shape, color etc.) for node labels.
|
||
|
|
|
||
|
|
horizontalalignment : string (default='center')
|
||
|
|
Horizontal alignment {'center', 'right', 'left'}
|
||
|
|
|
||
|
|
verticalalignment : string (default='center')
|
||
|
|
Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}
|
||
|
|
|
||
|
|
ax : Matplotlib Axes object, optional
|
||
|
|
Draw the graph in the specified Matplotlib axes.
|
||
|
|
|
||
|
|
clip_on : bool (default=True)
|
||
|
|
Turn on clipping of node labels at axis boundaries
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
dict
|
||
|
|
`dict` of labels keyed on the nodes
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> G = nx.dodecahedral_graph()
|
||
|
|
>>> labels = nx.draw_networkx_labels(G, pos=nx.spring_layout(G))
|
||
|
|
|
||
|
|
Also see the NetworkX drawing examples at
|
||
|
|
https://networkx.org/documentation/latest/auto_examples/index.html
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
draw
|
||
|
|
draw_networkx
|
||
|
|
draw_networkx_nodes
|
||
|
|
draw_networkx_edges
|
||
|
|
draw_networkx_edge_labels
|
||
|
|
"""
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
|
||
|
|
if ax is None:
|
||
|
|
ax = plt.gca()
|
||
|
|
|
||
|
|
if labels is None:
|
||
|
|
labels = {n: n for n in G.nodes()}
|
||
|
|
|
||
|
|
text_items = {} # there is no text collection so we'll fake one
|
||
|
|
for n, label in labels.items():
|
||
|
|
(x, y) = pos[n]
|
||
|
|
if not isinstance(label, str):
|
||
|
|
label = str(label) # this makes "1" and 1 labeled the same
|
||
|
|
t = ax.text(
|
||
|
|
x,
|
||
|
|
y,
|
||
|
|
label,
|
||
|
|
size=font_size,
|
||
|
|
color=font_color,
|
||
|
|
family=font_family,
|
||
|
|
weight=font_weight,
|
||
|
|
alpha=alpha,
|
||
|
|
horizontalalignment=horizontalalignment,
|
||
|
|
verticalalignment=verticalalignment,
|
||
|
|
transform=ax.transData,
|
||
|
|
bbox=bbox,
|
||
|
|
clip_on=clip_on,
|
||
|
|
)
|
||
|
|
text_items[n] = t
|
||
|
|
|
||
|
|
ax.tick_params(
|
||
|
|
axis="both",
|
||
|
|
which="both",
|
||
|
|
bottom=False,
|
||
|
|
left=False,
|
||
|
|
labelbottom=False,
|
||
|
|
labelleft=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
return text_items
|
||
|
|
|
||
|
|
|
||
|
|
def draw_networkx_edge_labels(
|
||
|
|
G,
|
||
|
|
pos,
|
||
|
|
edge_labels=None,
|
||
|
|
label_pos=0.5,
|
||
|
|
font_size=10,
|
||
|
|
font_color="k",
|
||
|
|
font_family="sans-serif",
|
||
|
|
font_weight="normal",
|
||
|
|
alpha=None,
|
||
|
|
bbox=None,
|
||
|
|
horizontalalignment="center",
|
||
|
|
verticalalignment="center",
|
||
|
|
ax=None,
|
||
|
|
rotate=True,
|
||
|
|
clip_on=True,
|
||
|
|
):
|
||
|
|
"""Draw edge labels.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : graph
|
||
|
|
A networkx graph
|
||
|
|
|
||
|
|
pos : dictionary
|
||
|
|
A dictionary with nodes as keys and positions as values.
|
||
|
|
Positions should be sequences of length 2.
|
||
|
|
|
||
|
|
edge_labels : dictionary (default=None)
|
||
|
|
Edge labels in a dictionary of labels keyed by edge two-tuple.
|
||
|
|
Only labels for the keys in the dictionary are drawn.
|
||
|
|
|
||
|
|
label_pos : float (default=0.5)
|
||
|
|
Position of edge label along edge (0=head, 0.5=center, 1=tail)
|
||
|
|
|
||
|
|
font_size : int (default=10)
|
||
|
|
Font size for text labels
|
||
|
|
|
||
|
|
font_color : string (default='k' black)
|
||
|
|
Font color string
|
||
|
|
|
||
|
|
font_weight : string (default='normal')
|
||
|
|
Font weight
|
||
|
|
|
||
|
|
font_family : string (default='sans-serif')
|
||
|
|
Font family
|
||
|
|
|
||
|
|
alpha : float or None (default=None)
|
||
|
|
The text transparency
|
||
|
|
|
||
|
|
bbox : Matplotlib bbox, optional
|
||
|
|
Specify text box properties (e.g. shape, color etc.) for edge labels.
|
||
|
|
Default is {boxstyle='round', ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0)}.
|
||
|
|
|
||
|
|
horizontalalignment : string (default='center')
|
||
|
|
Horizontal alignment {'center', 'right', 'left'}
|
||
|
|
|
||
|
|
verticalalignment : string (default='center')
|
||
|
|
Vertical alignment {'center', 'top', 'bottom', 'baseline', 'center_baseline'}
|
||
|
|
|
||
|
|
ax : Matplotlib Axes object, optional
|
||
|
|
Draw the graph in the specified Matplotlib axes.
|
||
|
|
|
||
|
|
rotate : bool (deafult=True)
|
||
|
|
Rotate edge labels to lie parallel to edges
|
||
|
|
|
||
|
|
clip_on : bool (default=True)
|
||
|
|
Turn on clipping of edge labels at axis boundaries
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
dict
|
||
|
|
`dict` of labels keyed by edge
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> G = nx.dodecahedral_graph()
|
||
|
|
>>> edge_labels = nx.draw_networkx_edge_labels(G, pos=nx.spring_layout(G))
|
||
|
|
|
||
|
|
Also see the NetworkX drawing examples at
|
||
|
|
https://networkx.org/documentation/latest/auto_examples/index.html
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
draw
|
||
|
|
draw_networkx
|
||
|
|
draw_networkx_nodes
|
||
|
|
draw_networkx_edges
|
||
|
|
draw_networkx_labels
|
||
|
|
"""
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
if ax is None:
|
||
|
|
ax = plt.gca()
|
||
|
|
if edge_labels is None:
|
||
|
|
labels = {(u, v): d for u, v, d in G.edges(data=True)}
|
||
|
|
else:
|
||
|
|
labels = edge_labels
|
||
|
|
# Informative exception for multiedges
|
||
|
|
try:
|
||
|
|
(u, v) = next(iter(labels)) # ensures no edge key provided
|
||
|
|
except ValueError as err:
|
||
|
|
raise nx.NetworkXError(
|
||
|
|
"draw_networkx_edge_labels does not support multiedges."
|
||
|
|
) from err
|
||
|
|
except StopIteration:
|
||
|
|
pass
|
||
|
|
|
||
|
|
text_items = {}
|
||
|
|
for (n1, n2), label in labels.items():
|
||
|
|
(x1, y1) = pos[n1]
|
||
|
|
(x2, y2) = pos[n2]
|
||
|
|
(x, y) = (
|
||
|
|
x1 * label_pos + x2 * (1.0 - label_pos),
|
||
|
|
y1 * label_pos + y2 * (1.0 - label_pos),
|
||
|
|
)
|
||
|
|
|
||
|
|
if rotate:
|
||
|
|
# in degrees
|
||
|
|
angle = np.arctan2(y2 - y1, x2 - x1) / (2.0 * np.pi) * 360
|
||
|
|
# make label orientation "right-side-up"
|
||
|
|
if angle > 90:
|
||
|
|
angle -= 180
|
||
|
|
if angle < -90:
|
||
|
|
angle += 180
|
||
|
|
# transform data coordinate angle to screen coordinate angle
|
||
|
|
xy = np.array((x, y))
|
||
|
|
trans_angle = ax.transData.transform_angles(
|
||
|
|
np.array((angle,)), xy.reshape((1, 2))
|
||
|
|
)[0]
|
||
|
|
else:
|
||
|
|
trans_angle = 0.0
|
||
|
|
# use default box of white with white border
|
||
|
|
if bbox is None:
|
||
|
|
bbox = dict(boxstyle="round", ec=(1.0, 1.0, 1.0), fc=(1.0, 1.0, 1.0))
|
||
|
|
if not isinstance(label, str):
|
||
|
|
label = str(label) # this makes "1" and 1 labeled the same
|
||
|
|
|
||
|
|
t = ax.text(
|
||
|
|
x,
|
||
|
|
y,
|
||
|
|
label,
|
||
|
|
size=font_size,
|
||
|
|
color=font_color,
|
||
|
|
family=font_family,
|
||
|
|
weight=font_weight,
|
||
|
|
alpha=alpha,
|
||
|
|
horizontalalignment=horizontalalignment,
|
||
|
|
verticalalignment=verticalalignment,
|
||
|
|
rotation=trans_angle,
|
||
|
|
transform=ax.transData,
|
||
|
|
bbox=bbox,
|
||
|
|
zorder=1,
|
||
|
|
clip_on=clip_on,
|
||
|
|
)
|
||
|
|
text_items[(n1, n2)] = t
|
||
|
|
|
||
|
|
ax.tick_params(
|
||
|
|
axis="both",
|
||
|
|
which="both",
|
||
|
|
bottom=False,
|
||
|
|
left=False,
|
||
|
|
labelbottom=False,
|
||
|
|
labelleft=False,
|
||
|
|
)
|
||
|
|
|
||
|
|
return text_items
|
||
|
|
|
||
|
|
|
||
|
|
def draw_circular(G, **kwargs):
|
||
|
|
"""Draw the graph `G` with a circular layout.
|
||
|
|
|
||
|
|
This is a convenience function equivalent to::
|
||
|
|
|
||
|
|
nx.draw(G, pos=nx.circular_layout(G), **kwargs)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : graph
|
||
|
|
A networkx graph
|
||
|
|
|
||
|
|
kwargs : optional keywords
|
||
|
|
See `draw_networkx` for a description of optional keywords.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The layout is computed each time this function is called. For
|
||
|
|
repeated drawing it is much more efficient to call
|
||
|
|
`~networkx.drawing.layout.circular_layout` directly and reuse the result::
|
||
|
|
|
||
|
|
>>> G = nx.complete_graph(5)
|
||
|
|
>>> pos = nx.circular_layout(G)
|
||
|
|
>>> nx.draw(G, pos=pos) # Draw the original graph
|
||
|
|
>>> # Draw a subgraph, reusing the same node positions
|
||
|
|
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
:func:`~networkx.drawing.layout.circular_layout`
|
||
|
|
"""
|
||
|
|
draw(G, circular_layout(G), **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def draw_kamada_kawai(G, **kwargs):
|
||
|
|
"""Draw the graph `G` with a Kamada-Kawai force-directed layout.
|
||
|
|
|
||
|
|
This is a convenience function equivalent to::
|
||
|
|
|
||
|
|
nx.draw(G, pos=nx.kamada_kawai_layout(G), **kwargs)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : graph
|
||
|
|
A networkx graph
|
||
|
|
|
||
|
|
kwargs : optional keywords
|
||
|
|
See `draw_networkx` for a description of optional keywords.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The layout is computed each time this function is called.
|
||
|
|
For repeated drawing it is much more efficient to call
|
||
|
|
`~networkx.drawing.layout.kamada_kawai_layout` directly and reuse the
|
||
|
|
result::
|
||
|
|
|
||
|
|
>>> G = nx.complete_graph(5)
|
||
|
|
>>> pos = nx.kamada_kawai_layout(G)
|
||
|
|
>>> nx.draw(G, pos=pos) # Draw the original graph
|
||
|
|
>>> # Draw a subgraph, reusing the same node positions
|
||
|
|
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
:func:`~networkx.drawing.layout.kamada_kawai_layout`
|
||
|
|
"""
|
||
|
|
draw(G, kamada_kawai_layout(G), **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def draw_random(G, **kwargs):
|
||
|
|
"""Draw the graph `G` with a random layout.
|
||
|
|
|
||
|
|
This is a convenience function equivalent to::
|
||
|
|
|
||
|
|
nx.draw(G, pos=nx.random_layout(G), **kwargs)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : graph
|
||
|
|
A networkx graph
|
||
|
|
|
||
|
|
kwargs : optional keywords
|
||
|
|
See `draw_networkx` for a description of optional keywords.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The layout is computed each time this function is called.
|
||
|
|
For repeated drawing it is much more efficient to call
|
||
|
|
`~networkx.drawing.layout.random_layout` directly and reuse the result::
|
||
|
|
|
||
|
|
>>> G = nx.complete_graph(5)
|
||
|
|
>>> pos = nx.random_layout(G)
|
||
|
|
>>> nx.draw(G, pos=pos) # Draw the original graph
|
||
|
|
>>> # Draw a subgraph, reusing the same node positions
|
||
|
|
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
:func:`~networkx.drawing.layout.random_layout`
|
||
|
|
"""
|
||
|
|
draw(G, random_layout(G), **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def draw_spectral(G, **kwargs):
|
||
|
|
"""Draw the graph `G` with a spectral 2D layout.
|
||
|
|
|
||
|
|
This is a convenience function equivalent to::
|
||
|
|
|
||
|
|
nx.draw(G, pos=nx.spectral_layout(G), **kwargs)
|
||
|
|
|
||
|
|
For more information about how node positions are determined, see
|
||
|
|
`~networkx.drawing.layout.spectral_layout`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : graph
|
||
|
|
A networkx graph
|
||
|
|
|
||
|
|
kwargs : optional keywords
|
||
|
|
See `draw_networkx` for a description of optional keywords.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The layout is computed each time this function is called.
|
||
|
|
For repeated drawing it is much more efficient to call
|
||
|
|
`~networkx.drawing.layout.spectral_layout` directly and reuse the result::
|
||
|
|
|
||
|
|
>>> G = nx.complete_graph(5)
|
||
|
|
>>> pos = nx.spectral_layout(G)
|
||
|
|
>>> nx.draw(G, pos=pos) # Draw the original graph
|
||
|
|
>>> # Draw a subgraph, reusing the same node positions
|
||
|
|
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
:func:`~networkx.drawing.layout.spectral_layout`
|
||
|
|
"""
|
||
|
|
draw(G, spectral_layout(G), **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def draw_spring(G, **kwargs):
|
||
|
|
"""Draw the graph `G` with a spring layout.
|
||
|
|
|
||
|
|
This is a convenience function equivalent to::
|
||
|
|
|
||
|
|
nx.draw(G, pos=nx.spring_layout(G), **kwargs)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : graph
|
||
|
|
A networkx graph
|
||
|
|
|
||
|
|
kwargs : optional keywords
|
||
|
|
See `draw_networkx` for a description of optional keywords.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
`~networkx.drawing.layout.spring_layout` is also the default layout for
|
||
|
|
`draw`, so this function is equivalent to `draw`.
|
||
|
|
|
||
|
|
The layout is computed each time this function is called.
|
||
|
|
For repeated drawing it is much more efficient to call
|
||
|
|
`~networkx.drawing.layout.spring_layout` directly and reuse the result::
|
||
|
|
|
||
|
|
>>> G = nx.complete_graph(5)
|
||
|
|
>>> pos = nx.spring_layout(G)
|
||
|
|
>>> nx.draw(G, pos=pos) # Draw the original graph
|
||
|
|
>>> # Draw a subgraph, reusing the same node positions
|
||
|
|
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
draw
|
||
|
|
:func:`~networkx.drawing.layout.spring_layout`
|
||
|
|
"""
|
||
|
|
draw(G, spring_layout(G), **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def draw_shell(G, nlist=None, **kwargs):
|
||
|
|
"""Draw networkx graph `G` with shell layout.
|
||
|
|
|
||
|
|
This is a convenience function equivalent to::
|
||
|
|
|
||
|
|
nx.draw(G, pos=nx.shell_layout(G, nlist=nlist), **kwargs)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : graph
|
||
|
|
A networkx graph
|
||
|
|
|
||
|
|
nlist : list of list of nodes, optional
|
||
|
|
A list containing lists of nodes representing the shells.
|
||
|
|
Default is `None`, meaning all nodes are in a single shell.
|
||
|
|
See `~networkx.drawing.layout.shell_layout` for details.
|
||
|
|
|
||
|
|
kwargs : optional keywords
|
||
|
|
See `draw_networkx` for a description of optional keywords.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The layout is computed each time this function is called.
|
||
|
|
For repeated drawing it is much more efficient to call
|
||
|
|
`~networkx.drawing.layout.shell_layout` directly and reuse the result::
|
||
|
|
|
||
|
|
>>> G = nx.complete_graph(5)
|
||
|
|
>>> pos = nx.shell_layout(G)
|
||
|
|
>>> nx.draw(G, pos=pos) # Draw the original graph
|
||
|
|
>>> # Draw a subgraph, reusing the same node positions
|
||
|
|
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
:func:`~networkx.drawing.layout.shell_layout`
|
||
|
|
"""
|
||
|
|
draw(G, shell_layout(G, nlist=nlist), **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def draw_planar(G, **kwargs):
|
||
|
|
"""Draw a planar networkx graph `G` with planar layout.
|
||
|
|
|
||
|
|
This is a convenience function equivalent to::
|
||
|
|
|
||
|
|
nx.draw(G, pos=nx.planar_layout(G), **kwargs)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
G : graph
|
||
|
|
A planar networkx graph
|
||
|
|
|
||
|
|
kwargs : optional keywords
|
||
|
|
See `draw_networkx` for a description of optional keywords.
|
||
|
|
|
||
|
|
Raises
|
||
|
|
------
|
||
|
|
NetworkXException
|
||
|
|
When `G` is not planar
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The layout is computed each time this function is called.
|
||
|
|
For repeated drawing it is much more efficient to call
|
||
|
|
`~networkx.drawing.layout.planar_layout` directly and reuse the result::
|
||
|
|
|
||
|
|
>>> G = nx.path_graph(5)
|
||
|
|
>>> pos = nx.planar_layout(G)
|
||
|
|
>>> nx.draw(G, pos=pos) # Draw the original graph
|
||
|
|
>>> # Draw a subgraph, reusing the same node positions
|
||
|
|
>>> nx.draw(G.subgraph([0, 1, 2]), pos=pos, node_color="red")
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
:func:`~networkx.drawing.layout.planar_layout`
|
||
|
|
"""
|
||
|
|
draw(G, planar_layout(G), **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def apply_alpha(colors, alpha, elem_list, cmap=None, vmin=None, vmax=None):
|
||
|
|
"""Apply an alpha (or list of alphas) to the colors provided.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
|
||
|
|
colors : color string or array of floats (default='r')
|
||
|
|
Color of element. Can be a single color format string,
|
||
|
|
or a sequence of colors with the same length as nodelist.
|
||
|
|
If numeric values are specified they will be mapped to
|
||
|
|
colors using the cmap and vmin,vmax parameters. See
|
||
|
|
matplotlib.scatter for more details.
|
||
|
|
|
||
|
|
alpha : float or array of floats
|
||
|
|
Alpha values for elements. This can be a single alpha value, in
|
||
|
|
which case it will be applied to all the elements of color. Otherwise,
|
||
|
|
if it is an array, the elements of alpha will be applied to the colors
|
||
|
|
in order (cycling through alpha multiple times if necessary).
|
||
|
|
|
||
|
|
elem_list : array of networkx objects
|
||
|
|
The list of elements which are being colored. These could be nodes,
|
||
|
|
edges or labels.
|
||
|
|
|
||
|
|
cmap : matplotlib colormap
|
||
|
|
Color map for use if colors is a list of floats corresponding to points
|
||
|
|
on a color mapping.
|
||
|
|
|
||
|
|
vmin, vmax : float
|
||
|
|
Minimum and maximum values for normalizing colors if a colormap is used
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
|
||
|
|
rgba_colors : numpy ndarray
|
||
|
|
Array containing RGBA format values for each of the node colours.
|
||
|
|
|
||
|
|
"""
|
||
|
|
from itertools import cycle, islice
|
||
|
|
|
||
|
|
import matplotlib as mpl
|
||
|
|
import matplotlib.cm # call as mpl.cm
|
||
|
|
import matplotlib.colors # call as mpl.colors
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
# If we have been provided with a list of numbers as long as elem_list,
|
||
|
|
# apply the color mapping.
|
||
|
|
if len(colors) == len(elem_list) and isinstance(colors[0], Number):
|
||
|
|
mapper = mpl.cm.ScalarMappable(cmap=cmap)
|
||
|
|
mapper.set_clim(vmin, vmax)
|
||
|
|
rgba_colors = mapper.to_rgba(colors)
|
||
|
|
# Otherwise, convert colors to matplotlib's RGB using the colorConverter
|
||
|
|
# object. These are converted to numpy ndarrays to be consistent with the
|
||
|
|
# to_rgba method of ScalarMappable.
|
||
|
|
else:
|
||
|
|
try:
|
||
|
|
rgba_colors = np.array([mpl.colors.colorConverter.to_rgba(colors)])
|
||
|
|
except ValueError:
|
||
|
|
rgba_colors = np.array(
|
||
|
|
[mpl.colors.colorConverter.to_rgba(color) for color in colors]
|
||
|
|
)
|
||
|
|
# Set the final column of the rgba_colors to have the relevant alpha values
|
||
|
|
try:
|
||
|
|
# If alpha is longer than the number of colors, resize to the number of
|
||
|
|
# elements. Also, if rgba_colors.size (the number of elements of
|
||
|
|
# rgba_colors) is the same as the number of elements, resize the array,
|
||
|
|
# to avoid it being interpreted as a colormap by scatter()
|
||
|
|
if len(alpha) > len(rgba_colors) or rgba_colors.size == len(elem_list):
|
||
|
|
rgba_colors = np.resize(rgba_colors, (len(elem_list), 4))
|
||
|
|
rgba_colors[1:, 0] = rgba_colors[0, 0]
|
||
|
|
rgba_colors[1:, 1] = rgba_colors[0, 1]
|
||
|
|
rgba_colors[1:, 2] = rgba_colors[0, 2]
|
||
|
|
rgba_colors[:, 3] = list(islice(cycle(alpha), len(rgba_colors)))
|
||
|
|
except TypeError:
|
||
|
|
rgba_colors[:, -1] = alpha
|
||
|
|
return rgba_colors
|